Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [2]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading celeba: 0.00B [00:00, ?B/s]
Found mnist Data
Downloading celeba: 1.44GB [01:19, 18.2MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [1]:
import helper
data_dir = './data'
In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fcf9b58da20>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fcf92e27978>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real') 
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, (), name='learning_rate')
    return inputs_real, inputs_z, learning_rate

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha = 0.2
    
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 56, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x56

        x2 = tf.layers.conv2d(relu1, 112, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x112

        x3 = tf.layers.conv2d(relu2, 224, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x224

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*224))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.2
    
    with tf.variable_scope('generator', reuse=not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*2048)
        
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 2048))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 1024, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 512, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
       
        x4 = tf.layers.conv2d_transpose(x3, 256, 5, strides=2, padding='same')
        x4 = tf.layers.batch_normalization(x4, training=is_train)
        x4 = tf.maximum(alpha * x4, x4)
        
        #x5 = tf.layers.conv2d_transpose(x4, 128, 5, strides=2, padding='same')
        #x5 = tf.layers.batch_normalization(x5, training=is_train)
        #x5 = tf.maximum(alpha * x5, x5)
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x4, out_channel_dim, 5, strides=2, padding='same')
        logits = tf.image.resize_images(logits, (28, 28))
        # 28x28x out_channel_dim
        
        out = tf.tanh(logits)
        
        return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function

    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    steps = 0
    print_every = 10
    show_every=10
    sample_images=12
    
    #tf.reset_default_graph()
        
    input_real, input_z, learning_rate_tf = model_inputs(
        image_width=data_shape[1],
        image_height=data_shape[2],
        image_channels=data_shape[3],
        z_dim=z_dim
    )

    out_channel_dim=data_shape[3]
    d_loss, g_loss = model_loss(input_real, input_z, out_channel_dim)
        
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate_tf, beta1)    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Rescale batch_images to be in range -1 to 1
                batch_images *= 2
                
                # Run optimizers
                _ = sess.run(
                    d_opt, 
                    feed_dict={
                        input_real: batch_images,
                        input_z: batch_z,
                        learning_rate_tf: learning_rate
                    }
                )
                _ = sess.run(
                    g_opt, 
                    feed_dict={
                        input_z: batch_z,
                        input_real: batch_images,
                        learning_rate_tf: learning_rate
                    }
                )

                # added second generator based on this forum post
                # https://discussions.udacity.com/t/you-should-increase-batch-size-by-a-factor-of-two-inside-the-inner-for-loop/634945
                #_ = sess.run(
                #    g_opt, 
                #    feed_dict={
                #        input_z: batch_z,
                #        input_real: batch_images,
                #        learning_rate_tf: learning_rate
                #    }
                #)

                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, sample_images, input_z, out_channel_dim, data_image_mode)
               

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [17]:
batch_size = 64
z_dim = 100
learning_rate = 0.0005
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))

with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.0255... Generator Loss: 10.1298
Epoch 1/2... Discriminator Loss: 0.0421... Generator Loss: 4.3587
Epoch 1/2... Discriminator Loss: 0.0091... Generator Loss: 8.0690
Epoch 1/2... Discriminator Loss: 0.1887... Generator Loss: 5.0901
Epoch 1/2... Discriminator Loss: 0.1854... Generator Loss: 3.3298
Epoch 1/2... Discriminator Loss: 0.1372... Generator Loss: 3.8748
Epoch 1/2... Discriminator Loss: 2.0325... Generator Loss: 0.4019
Epoch 1/2... Discriminator Loss: 1.5917... Generator Loss: 1.4412
Epoch 1/2... Discriminator Loss: 1.0253... Generator Loss: 1.2293
Epoch 1/2... Discriminator Loss: 1.2098... Generator Loss: 0.7304
Epoch 1/2... Discriminator Loss: 0.7490... Generator Loss: 2.3247
Epoch 1/2... Discriminator Loss: 0.9812... Generator Loss: 0.8799
Epoch 1/2... Discriminator Loss: 1.6432... Generator Loss: 0.3640
Epoch 1/2... Discriminator Loss: 0.6118... Generator Loss: 1.5014
Epoch 1/2... Discriminator Loss: 1.1327... Generator Loss: 0.8037
Epoch 1/2... Discriminator Loss: 1.0556... Generator Loss: 1.3016
Epoch 1/2... Discriminator Loss: 0.7973... Generator Loss: 0.8946
Epoch 1/2... Discriminator Loss: 1.2307... Generator Loss: 0.6939
Epoch 1/2... Discriminator Loss: 1.7105... Generator Loss: 3.2797
Epoch 1/2... Discriminator Loss: 0.9740... Generator Loss: 1.6288
Epoch 1/2... Discriminator Loss: 1.1800... Generator Loss: 0.5714
Epoch 1/2... Discriminator Loss: 1.0423... Generator Loss: 1.2419
Epoch 1/2... Discriminator Loss: 0.8303... Generator Loss: 1.0144
Epoch 1/2... Discriminator Loss: 0.8842... Generator Loss: 1.2860
Epoch 1/2... Discriminator Loss: 1.1427... Generator Loss: 0.7137
Epoch 1/2... Discriminator Loss: 0.9713... Generator Loss: 1.0759
Epoch 1/2... Discriminator Loss: 1.4509... Generator Loss: 0.4452
Epoch 1/2... Discriminator Loss: 1.1499... Generator Loss: 0.5795
Epoch 1/2... Discriminator Loss: 1.0975... Generator Loss: 0.8712
Epoch 1/2... Discriminator Loss: 1.1129... Generator Loss: 0.8309
Epoch 1/2... Discriminator Loss: 1.2247... Generator Loss: 0.6403
Epoch 1/2... Discriminator Loss: 1.0692... Generator Loss: 0.6935
Epoch 1/2... Discriminator Loss: 1.1322... Generator Loss: 1.4839
Epoch 1/2... Discriminator Loss: 1.1732... Generator Loss: 0.6453
Epoch 1/2... Discriminator Loss: 1.1012... Generator Loss: 0.8773
Epoch 1/2... Discriminator Loss: 1.1594... Generator Loss: 1.1357
Epoch 1/2... Discriminator Loss: 0.8083... Generator Loss: 1.2838
Epoch 1/2... Discriminator Loss: 1.1500... Generator Loss: 1.0208
Epoch 1/2... Discriminator Loss: 1.3200... Generator Loss: 0.8729
Epoch 1/2... Discriminator Loss: 1.3389... Generator Loss: 0.5752
Epoch 1/2... Discriminator Loss: 1.2509... Generator Loss: 0.6307
Epoch 1/2... Discriminator Loss: 1.4131... Generator Loss: 0.4956
Epoch 1/2... Discriminator Loss: 1.1169... Generator Loss: 0.8022
Epoch 1/2... Discriminator Loss: 1.2366... Generator Loss: 1.2844
Epoch 1/2... Discriminator Loss: 1.0919... Generator Loss: 1.2410
Epoch 1/2... Discriminator Loss: 1.1155... Generator Loss: 0.6711
Epoch 1/2... Discriminator Loss: 1.3148... Generator Loss: 0.6119
Epoch 1/2... Discriminator Loss: 1.2788... Generator Loss: 1.1745
Epoch 1/2... Discriminator Loss: 1.1545... Generator Loss: 0.9787
Epoch 1/2... Discriminator Loss: 1.3972... Generator Loss: 0.4551
Epoch 1/2... Discriminator Loss: 1.1275... Generator Loss: 0.6729
Epoch 1/2... Discriminator Loss: 1.2821... Generator Loss: 0.7005
Epoch 1/2... Discriminator Loss: 1.1101... Generator Loss: 0.9349
Epoch 1/2... Discriminator Loss: 1.1501... Generator Loss: 1.0819
Epoch 1/2... Discriminator Loss: 1.1218... Generator Loss: 0.6044
Epoch 1/2... Discriminator Loss: 1.4568... Generator Loss: 0.4539
Epoch 1/2... Discriminator Loss: 1.0319... Generator Loss: 1.2832
Epoch 1/2... Discriminator Loss: 1.0913... Generator Loss: 0.8506
Epoch 1/2... Discriminator Loss: 1.2178... Generator Loss: 0.8175
Epoch 1/2... Discriminator Loss: 1.2704... Generator Loss: 0.9287
Epoch 1/2... Discriminator Loss: 1.0264... Generator Loss: 0.9049
Epoch 1/2... Discriminator Loss: 1.1034... Generator Loss: 0.8476
Epoch 1/2... Discriminator Loss: 1.3079... Generator Loss: 0.6508
Epoch 1/2... Discriminator Loss: 1.1988... Generator Loss: 1.2546
Epoch 1/2... Discriminator Loss: 1.2316... Generator Loss: 0.9332
Epoch 1/2... Discriminator Loss: 1.2513... Generator Loss: 1.0717
Epoch 1/2... Discriminator Loss: 1.1919... Generator Loss: 0.9267
Epoch 1/2... Discriminator Loss: 1.4062... Generator Loss: 1.2575
Epoch 1/2... Discriminator Loss: 1.0755... Generator Loss: 0.9888
Epoch 1/2... Discriminator Loss: 1.0964... Generator Loss: 0.7656
Epoch 1/2... Discriminator Loss: 1.1804... Generator Loss: 0.8636
Epoch 1/2... Discriminator Loss: 1.3503... Generator Loss: 0.5727
Epoch 1/2... Discriminator Loss: 1.1362... Generator Loss: 0.6009
Epoch 1/2... Discriminator Loss: 1.2333... Generator Loss: 0.4754
Epoch 1/2... Discriminator Loss: 1.0753... Generator Loss: 0.9745
Epoch 1/2... Discriminator Loss: 1.2596... Generator Loss: 0.5398
Epoch 1/2... Discriminator Loss: 1.0608... Generator Loss: 0.9438
Epoch 1/2... Discriminator Loss: 1.1309... Generator Loss: 0.7778
Epoch 1/2... Discriminator Loss: 1.0437... Generator Loss: 0.7321
Epoch 1/2... Discriminator Loss: 1.2568... Generator Loss: 0.7140
Epoch 1/2... Discriminator Loss: 1.1496... Generator Loss: 0.8892
Epoch 1/2... Discriminator Loss: 1.1094... Generator Loss: 0.8771
Epoch 1/2... Discriminator Loss: 1.1907... Generator Loss: 1.1275
Epoch 1/2... Discriminator Loss: 1.4307... Generator Loss: 0.4074
Epoch 1/2... Discriminator Loss: 1.1616... Generator Loss: 0.6634
Epoch 1/2... Discriminator Loss: 1.2562... Generator Loss: 0.7466
Epoch 1/2... Discriminator Loss: 1.2456... Generator Loss: 0.8969
Epoch 1/2... Discriminator Loss: 1.3254... Generator Loss: 0.4935
Epoch 1/2... Discriminator Loss: 1.1342... Generator Loss: 0.8136
Epoch 1/2... Discriminator Loss: 1.0489... Generator Loss: 0.9593
Epoch 1/2... Discriminator Loss: 1.2574... Generator Loss: 0.6317
Epoch 1/2... Discriminator Loss: 1.3451... Generator Loss: 0.6077
Epoch 1/2... Discriminator Loss: 1.2567... Generator Loss: 0.5928
Epoch 2/2... Discriminator Loss: 1.2536... Generator Loss: 0.5526
Epoch 2/2... Discriminator Loss: 1.3529... Generator Loss: 0.5384
Epoch 2/2... Discriminator Loss: 1.2081... Generator Loss: 0.6815
Epoch 2/2... Discriminator Loss: 1.2006... Generator Loss: 0.6999
Epoch 2/2... Discriminator Loss: 1.3393... Generator Loss: 0.9346
Epoch 2/2... Discriminator Loss: 1.1317... Generator Loss: 0.8539
Epoch 2/2... Discriminator Loss: 1.3215... Generator Loss: 0.5626
Epoch 2/2... Discriminator Loss: 1.1329... Generator Loss: 0.9055
Epoch 2/2... Discriminator Loss: 1.2802... Generator Loss: 0.7999
Epoch 2/2... Discriminator Loss: 1.2731... Generator Loss: 0.5432
Epoch 2/2... Discriminator Loss: 1.2521... Generator Loss: 0.6423
Epoch 2/2... Discriminator Loss: 1.2621... Generator Loss: 0.5226
Epoch 2/2... Discriminator Loss: 1.1789... Generator Loss: 1.0875
Epoch 2/2... Discriminator Loss: 1.2100... Generator Loss: 0.5612
Epoch 2/2... Discriminator Loss: 1.2925... Generator Loss: 1.3085
Epoch 2/2... Discriminator Loss: 1.2909... Generator Loss: 0.4947
Epoch 2/2... Discriminator Loss: 1.1906... Generator Loss: 1.5838
Epoch 2/2... Discriminator Loss: 1.2792... Generator Loss: 0.5473
Epoch 2/2... Discriminator Loss: 1.0785... Generator Loss: 0.9557
Epoch 2/2... Discriminator Loss: 1.3474... Generator Loss: 0.5273
Epoch 2/2... Discriminator Loss: 1.1543... Generator Loss: 0.8009
Epoch 2/2... Discriminator Loss: 1.1347... Generator Loss: 0.6968
Epoch 2/2... Discriminator Loss: 0.8757... Generator Loss: 0.9900
Epoch 2/2... Discriminator Loss: 1.2399... Generator Loss: 1.4893
Epoch 2/2... Discriminator Loss: 1.0467... Generator Loss: 1.4401
Epoch 2/2... Discriminator Loss: 1.3465... Generator Loss: 1.2243
Epoch 2/2... Discriminator Loss: 1.2008... Generator Loss: 0.5122
Epoch 2/2... Discriminator Loss: 1.1584... Generator Loss: 0.5715
Epoch 2/2... Discriminator Loss: 1.5549... Generator Loss: 0.3448
Epoch 2/2... Discriminator Loss: 1.2472... Generator Loss: 0.5250
Epoch 2/2... Discriminator Loss: 1.1048... Generator Loss: 0.7593
Epoch 2/2... Discriminator Loss: 1.1550... Generator Loss: 0.8440
Epoch 2/2... Discriminator Loss: 1.1137... Generator Loss: 0.7970
Epoch 2/2... Discriminator Loss: 1.2136... Generator Loss: 0.7956
Epoch 2/2... Discriminator Loss: 1.2490... Generator Loss: 0.7713
Epoch 2/2... Discriminator Loss: 1.1462... Generator Loss: 0.6993
Epoch 2/2... Discriminator Loss: 1.1800... Generator Loss: 0.8696
Epoch 2/2... Discriminator Loss: 1.1472... Generator Loss: 0.8450
Epoch 2/2... Discriminator Loss: 1.1616... Generator Loss: 0.7436
Epoch 2/2... Discriminator Loss: 1.2342... Generator Loss: 0.6240
Epoch 2/2... Discriminator Loss: 1.3353... Generator Loss: 0.8493
Epoch 2/2... Discriminator Loss: 1.4254... Generator Loss: 0.5039
Epoch 2/2... Discriminator Loss: 1.1836... Generator Loss: 1.0656
Epoch 2/2... Discriminator Loss: 1.2280... Generator Loss: 0.6912
Epoch 2/2... Discriminator Loss: 1.2471... Generator Loss: 0.7927
Epoch 2/2... Discriminator Loss: 1.1761... Generator Loss: 0.8529
Epoch 2/2... Discriminator Loss: 1.4385... Generator Loss: 0.4568
Epoch 2/2... Discriminator Loss: 1.3075... Generator Loss: 0.5783
Epoch 2/2... Discriminator Loss: 1.4463... Generator Loss: 0.8020
Epoch 2/2... Discriminator Loss: 1.3261... Generator Loss: 0.5216
Epoch 2/2... Discriminator Loss: 1.3507... Generator Loss: 0.6452
Epoch 2/2... Discriminator Loss: 1.2184... Generator Loss: 0.6971
Epoch 2/2... Discriminator Loss: 1.2784... Generator Loss: 0.8094
Epoch 2/2... Discriminator Loss: 1.4931... Generator Loss: 0.5178
Epoch 2/2... Discriminator Loss: 1.2369... Generator Loss: 0.6288
Epoch 2/2... Discriminator Loss: 1.2973... Generator Loss: 0.7255
Epoch 2/2... Discriminator Loss: 1.2087... Generator Loss: 0.6750
Epoch 2/2... Discriminator Loss: 1.1307... Generator Loss: 1.3180
Epoch 2/2... Discriminator Loss: 1.1879... Generator Loss: 0.6767
Epoch 2/2... Discriminator Loss: 1.5019... Generator Loss: 0.3498
Epoch 2/2... Discriminator Loss: 1.3508... Generator Loss: 0.8041
Epoch 2/2... Discriminator Loss: 1.2964... Generator Loss: 1.1100
Epoch 2/2... Discriminator Loss: 1.2424... Generator Loss: 1.0263
Epoch 2/2... Discriminator Loss: 1.2764... Generator Loss: 0.7292
Epoch 2/2... Discriminator Loss: 1.3167... Generator Loss: 0.5611
Epoch 2/2... Discriminator Loss: 1.1059... Generator Loss: 0.7555
Epoch 2/2... Discriminator Loss: 2.0822... Generator Loss: 0.1812
Epoch 2/2... Discriminator Loss: 1.2562... Generator Loss: 0.6617
Epoch 2/2... Discriminator Loss: 1.2850... Generator Loss: 0.5382
Epoch 2/2... Discriminator Loss: 1.1926... Generator Loss: 0.7409
Epoch 2/2... Discriminator Loss: 1.1507... Generator Loss: 0.8777
Epoch 2/2... Discriminator Loss: 1.3983... Generator Loss: 0.4762
Epoch 2/2... Discriminator Loss: 1.0737... Generator Loss: 0.7460
Epoch 2/2... Discriminator Loss: 1.4798... Generator Loss: 0.4296
Epoch 2/2... Discriminator Loss: 1.0764... Generator Loss: 0.8096
Epoch 2/2... Discriminator Loss: 1.0915... Generator Loss: 0.8915
Epoch 2/2... Discriminator Loss: 0.9649... Generator Loss: 1.0143
Epoch 2/2... Discriminator Loss: 1.1215... Generator Loss: 0.8735
Epoch 2/2... Discriminator Loss: 1.3903... Generator Loss: 0.4580
Epoch 2/2... Discriminator Loss: 1.3646... Generator Loss: 0.4959
Epoch 2/2... Discriminator Loss: 1.0641... Generator Loss: 0.8965
Epoch 2/2... Discriminator Loss: 1.2354... Generator Loss: 0.8743
Epoch 2/2... Discriminator Loss: 1.1616... Generator Loss: 1.0187
Epoch 2/2... Discriminator Loss: 1.4298... Generator Loss: 1.0225
Epoch 2/2... Discriminator Loss: 1.1575... Generator Loss: 0.8091
Epoch 2/2... Discriminator Loss: 1.3296... Generator Loss: 0.6177
Epoch 2/2... Discriminator Loss: 1.1757... Generator Loss: 1.3369
Epoch 2/2... Discriminator Loss: 1.1815... Generator Loss: 0.7771
Epoch 2/2... Discriminator Loss: 1.2182... Generator Loss: 0.7682
Epoch 2/2... Discriminator Loss: 1.2952... Generator Loss: 0.6214
Epoch 2/2... Discriminator Loss: 1.4257... Generator Loss: 0.4850
Epoch 2/2... Discriminator Loss: 1.3427... Generator Loss: 0.5817
Epoch 2/2... Discriminator Loss: 1.1149... Generator Loss: 0.9105
Epoch 2/2... Discriminator Loss: 1.2802... Generator Loss: 0.6956

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [12]:
batch_size = 64
z_dim = 100
learning_rate = 0.0005
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.0234... Generator Loss: 5.6711
Epoch 1/1... Discriminator Loss: 0.1931... Generator Loss: 3.7404
Epoch 1/1... Discriminator Loss: 2.9686... Generator Loss: 0.4619
Epoch 1/1... Discriminator Loss: 0.4098... Generator Loss: 8.0788
Epoch 1/1... Discriminator Loss: 0.0776... Generator Loss: 3.8534
Epoch 1/1... Discriminator Loss: 0.7787... Generator Loss: 4.2196
Epoch 1/1... Discriminator Loss: 0.9178... Generator Loss: 0.9584
Epoch 1/1... Discriminator Loss: 0.1836... Generator Loss: 2.9295
Epoch 1/1... Discriminator Loss: 0.2129... Generator Loss: 2.4524
Epoch 1/1... Discriminator Loss: 3.5951... Generator Loss: 12.9035
Epoch 1/1... Discriminator Loss: 2.5119... Generator Loss: 2.7067
Epoch 1/1... Discriminator Loss: 2.0688... Generator Loss: 0.2607
Epoch 1/1... Discriminator Loss: 1.8592... Generator Loss: 0.6647
Epoch 1/1... Discriminator Loss: 1.4895... Generator Loss: 0.6727
Epoch 1/1... Discriminator Loss: 1.6288... Generator Loss: 0.6286
Epoch 1/1... Discriminator Loss: 1.5845... Generator Loss: 0.5867
Epoch 1/1... Discriminator Loss: 1.5639... Generator Loss: 0.6527
Epoch 1/1... Discriminator Loss: 1.5374... Generator Loss: 0.5819
Epoch 1/1... Discriminator Loss: 1.6488... Generator Loss: 0.5969
Epoch 1/1... Discriminator Loss: 1.6709... Generator Loss: 0.6479
Epoch 1/1... Discriminator Loss: 1.3687... Generator Loss: 0.8217
Epoch 1/1... Discriminator Loss: 1.3804... Generator Loss: 0.8256
Epoch 1/1... Discriminator Loss: 1.5404... Generator Loss: 0.7237
Epoch 1/1... Discriminator Loss: 1.1972... Generator Loss: 0.8640
Epoch 1/1... Discriminator Loss: 1.7789... Generator Loss: 0.5501
Epoch 1/1... Discriminator Loss: 1.4484... Generator Loss: 0.6976
Epoch 1/1... Discriminator Loss: 1.4542... Generator Loss: 0.7324
Epoch 1/1... Discriminator Loss: 1.5631... Generator Loss: 0.6558
Epoch 1/1... Discriminator Loss: 1.2886... Generator Loss: 0.7295
Epoch 1/1... Discriminator Loss: 1.4921... Generator Loss: 0.6096
Epoch 1/1... Discriminator Loss: 1.3300... Generator Loss: 0.7068
Epoch 1/1... Discriminator Loss: 1.4569... Generator Loss: 0.5905
Epoch 1/1... Discriminator Loss: 1.4272... Generator Loss: 0.6885
Epoch 1/1... Discriminator Loss: 1.3776... Generator Loss: 0.7559
Epoch 1/1... Discriminator Loss: 1.4421... Generator Loss: 0.6725
Epoch 1/1... Discriminator Loss: 1.3238... Generator Loss: 0.7999
Epoch 1/1... Discriminator Loss: 1.4655... Generator Loss: 0.6216
Epoch 1/1... Discriminator Loss: 1.4079... Generator Loss: 0.6095
Epoch 1/1... Discriminator Loss: 1.6543... Generator Loss: 0.6728
Epoch 1/1... Discriminator Loss: 1.3143... Generator Loss: 0.7472
Epoch 1/1... Discriminator Loss: 1.5140... Generator Loss: 0.6070
Epoch 1/1... Discriminator Loss: 1.4627... Generator Loss: 0.6558
Epoch 1/1... Discriminator Loss: 1.3449... Generator Loss: 0.6869
Epoch 1/1... Discriminator Loss: 1.4555... Generator Loss: 0.6707
Epoch 1/1... Discriminator Loss: 1.5136... Generator Loss: 0.6091
Epoch 1/1... Discriminator Loss: 1.3012... Generator Loss: 0.6593
Epoch 1/1... Discriminator Loss: 1.3880... Generator Loss: 0.6782
Epoch 1/1... Discriminator Loss: 1.4722... Generator Loss: 0.6075
Epoch 1/1... Discriminator Loss: 1.4083... Generator Loss: 0.6694
Epoch 1/1... Discriminator Loss: 1.4109... Generator Loss: 0.6851
Epoch 1/1... Discriminator Loss: 1.5129... Generator Loss: 0.5761
Epoch 1/1... Discriminator Loss: 1.5295... Generator Loss: 0.6477
Epoch 1/1... Discriminator Loss: 1.3609... Generator Loss: 0.7276
Epoch 1/1... Discriminator Loss: 1.4971... Generator Loss: 0.6553
Epoch 1/1... Discriminator Loss: 1.4174... Generator Loss: 0.6707
Epoch 1/1... Discriminator Loss: 1.4711... Generator Loss: 0.6792
Epoch 1/1... Discriminator Loss: 1.3636... Generator Loss: 0.6919
Epoch 1/1... Discriminator Loss: 1.5239... Generator Loss: 0.6655
Epoch 1/1... Discriminator Loss: 1.4782... Generator Loss: 0.6352
Epoch 1/1... Discriminator Loss: 1.4651... Generator Loss: 0.6669
Epoch 1/1... Discriminator Loss: 1.4831... Generator Loss: 0.6941
Epoch 1/1... Discriminator Loss: 1.4027... Generator Loss: 0.6928
Epoch 1/1... Discriminator Loss: 1.4490... Generator Loss: 0.6331
Epoch 1/1... Discriminator Loss: 1.4298... Generator Loss: 0.6947
Epoch 1/1... Discriminator Loss: 1.4091... Generator Loss: 0.6756
Epoch 1/1... Discriminator Loss: 1.5062... Generator Loss: 0.6095
Epoch 1/1... Discriminator Loss: 1.4362... Generator Loss: 0.6360
Epoch 1/1... Discriminator Loss: 1.4108... Generator Loss: 0.6632
Epoch 1/1... Discriminator Loss: 1.4982... Generator Loss: 0.6979
Epoch 1/1... Discriminator Loss: 1.3507... Generator Loss: 0.7269
Epoch 1/1... Discriminator Loss: 1.5126... Generator Loss: 0.6362
Epoch 1/1... Discriminator Loss: 1.3900... Generator Loss: 0.6927
Epoch 1/1... Discriminator Loss: 1.5384... Generator Loss: 0.6775
Epoch 1/1... Discriminator Loss: 1.3400... Generator Loss: 0.7272
Epoch 1/1... Discriminator Loss: 1.4739... Generator Loss: 0.6762
Epoch 1/1... Discriminator Loss: 1.4593... Generator Loss: 0.6496
Epoch 1/1... Discriminator Loss: 1.4411... Generator Loss: 0.6225
Epoch 1/1... Discriminator Loss: 1.4752... Generator Loss: 0.5876
Epoch 1/1... Discriminator Loss: 1.3692... Generator Loss: 0.7170
Epoch 1/1... Discriminator Loss: 1.4396... Generator Loss: 0.6830
Epoch 1/1... Discriminator Loss: 1.3768... Generator Loss: 0.7045
Epoch 1/1... Discriminator Loss: 1.5077... Generator Loss: 0.6854
Epoch 1/1... Discriminator Loss: 1.3769... Generator Loss: 0.7447
Epoch 1/1... Discriminator Loss: 1.5629... Generator Loss: 0.6472
Epoch 1/1... Discriminator Loss: 1.4590... Generator Loss: 0.6500
Epoch 1/1... Discriminator Loss: 1.4103... Generator Loss: 0.6937
Epoch 1/1... Discriminator Loss: 1.4749... Generator Loss: 0.6612
Epoch 1/1... Discriminator Loss: 1.4229... Generator Loss: 0.6673
Epoch 1/1... Discriminator Loss: 1.4144... Generator Loss: 0.6585
Epoch 1/1... Discriminator Loss: 1.4316... Generator Loss: 0.6553
Epoch 1/1... Discriminator Loss: 1.4866... Generator Loss: 0.6455
Epoch 1/1... Discriminator Loss: 1.4556... Generator Loss: 0.6304
Epoch 1/1... Discriminator Loss: 1.4235... Generator Loss: 0.6372
Epoch 1/1... Discriminator Loss: 1.4286... Generator Loss: 0.6808
Epoch 1/1... Discriminator Loss: 1.5235... Generator Loss: 0.6436
Epoch 1/1... Discriminator Loss: 1.4405... Generator Loss: 0.6918
Epoch 1/1... Discriminator Loss: 1.4661... Generator Loss: 0.6799
Epoch 1/1... Discriminator Loss: 1.4103... Generator Loss: 0.7225
Epoch 1/1... Discriminator Loss: 1.4934... Generator Loss: 0.6191
Epoch 1/1... Discriminator Loss: 1.4247... Generator Loss: 0.7384
Epoch 1/1... Discriminator Loss: 1.4424... Generator Loss: 0.6969
Epoch 1/1... Discriminator Loss: 1.3787... Generator Loss: 0.7177
Epoch 1/1... Discriminator Loss: 1.4515... Generator Loss: 0.6937
Epoch 1/1... Discriminator Loss: 1.4354... Generator Loss: 0.6943
Epoch 1/1... Discriminator Loss: 1.4395... Generator Loss: 0.6660
Epoch 1/1... Discriminator Loss: 1.4128... Generator Loss: 0.6828
Epoch 1/1... Discriminator Loss: 1.3886... Generator Loss: 0.7285
Epoch 1/1... Discriminator Loss: 1.4658... Generator Loss: 0.7039
Epoch 1/1... Discriminator Loss: 1.4657... Generator Loss: 0.6821
Epoch 1/1... Discriminator Loss: 1.4027... Generator Loss: 0.6793
Epoch 1/1... Discriminator Loss: 1.4534... Generator Loss: 0.7617
Epoch 1/1... Discriminator Loss: 1.4633... Generator Loss: 0.6222
Epoch 1/1... Discriminator Loss: 1.4536... Generator Loss: 0.7351
Epoch 1/1... Discriminator Loss: 1.2983... Generator Loss: 0.7365
Epoch 1/1... Discriminator Loss: 1.4314... Generator Loss: 0.6394
Epoch 1/1... Discriminator Loss: 1.4279... Generator Loss: 0.6335
Epoch 1/1... Discriminator Loss: 1.4214... Generator Loss: 0.7173
Epoch 1/1... Discriminator Loss: 1.4425... Generator Loss: 0.6407
Epoch 1/1... Discriminator Loss: 1.4773... Generator Loss: 0.7107
Epoch 1/1... Discriminator Loss: 1.4939... Generator Loss: 0.6914
Epoch 1/1... Discriminator Loss: 1.4948... Generator Loss: 0.6886
Epoch 1/1... Discriminator Loss: 1.4010... Generator Loss: 0.7539
Epoch 1/1... Discriminator Loss: 1.3941... Generator Loss: 0.6551
Epoch 1/1... Discriminator Loss: 1.4100... Generator Loss: 0.6591
Epoch 1/1... Discriminator Loss: 1.3859... Generator Loss: 0.6709
Epoch 1/1... Discriminator Loss: 1.4701... Generator Loss: 0.6835
Epoch 1/1... Discriminator Loss: 1.3715... Generator Loss: 0.6903
Epoch 1/1... Discriminator Loss: 1.4030... Generator Loss: 0.6400
Epoch 1/1... Discriminator Loss: 1.3991... Generator Loss: 0.7221
Epoch 1/1... Discriminator Loss: 1.3862... Generator Loss: 0.7006
Epoch 1/1... Discriminator Loss: 1.4235... Generator Loss: 0.6717
Epoch 1/1... Discriminator Loss: 1.3788... Generator Loss: 0.6329
Epoch 1/1... Discriminator Loss: 1.3603... Generator Loss: 0.7706
Epoch 1/1... Discriminator Loss: 1.4221... Generator Loss: 0.6536
Epoch 1/1... Discriminator Loss: 1.4722... Generator Loss: 0.6486
Epoch 1/1... Discriminator Loss: 1.3679... Generator Loss: 0.6734
Epoch 1/1... Discriminator Loss: 1.3529... Generator Loss: 0.7202
Epoch 1/1... Discriminator Loss: 1.4815... Generator Loss: 0.6903
Epoch 1/1... Discriminator Loss: 1.4671... Generator Loss: 0.6039
Epoch 1/1... Discriminator Loss: 1.4340... Generator Loss: 0.6894
Epoch 1/1... Discriminator Loss: 1.4174... Generator Loss: 0.7217
Epoch 1/1... Discriminator Loss: 1.4036... Generator Loss: 0.6602
Epoch 1/1... Discriminator Loss: 1.4058... Generator Loss: 0.7215
Epoch 1/1... Discriminator Loss: 1.3889... Generator Loss: 0.6938
Epoch 1/1... Discriminator Loss: 1.4515... Generator Loss: 0.6576
Epoch 1/1... Discriminator Loss: 1.4668... Generator Loss: 0.6229
Epoch 1/1... Discriminator Loss: 1.3685... Generator Loss: 0.7034
Epoch 1/1... Discriminator Loss: 1.3622... Generator Loss: 0.6861
Epoch 1/1... Discriminator Loss: 1.4244... Generator Loss: 0.6635
Epoch 1/1... Discriminator Loss: 1.3842... Generator Loss: 0.7417
Epoch 1/1... Discriminator Loss: 1.3715... Generator Loss: 0.7025
Epoch 1/1... Discriminator Loss: 1.3823... Generator Loss: 0.6845
Epoch 1/1... Discriminator Loss: 1.4603... Generator Loss: 0.6661
Epoch 1/1... Discriminator Loss: 1.3558... Generator Loss: 0.7003
Epoch 1/1... Discriminator Loss: 1.3912... Generator Loss: 0.6880
Epoch 1/1... Discriminator Loss: 1.3696... Generator Loss: 0.7442
Epoch 1/1... Discriminator Loss: 1.4656... Generator Loss: 0.6158
Epoch 1/1... Discriminator Loss: 1.4144... Generator Loss: 0.7076
Epoch 1/1... Discriminator Loss: 1.4350... Generator Loss: 0.6904
Epoch 1/1... Discriminator Loss: 1.3890... Generator Loss: 0.7117
Epoch 1/1... Discriminator Loss: 1.3617... Generator Loss: 0.7845
Epoch 1/1... Discriminator Loss: 1.4007... Generator Loss: 0.7013
Epoch 1/1... Discriminator Loss: 1.4600... Generator Loss: 0.6221
Epoch 1/1... Discriminator Loss: 1.3750... Generator Loss: 0.7240
Epoch 1/1... Discriminator Loss: 1.3482... Generator Loss: 0.6954
Epoch 1/1... Discriminator Loss: 1.4649... Generator Loss: 0.6962
Epoch 1/1... Discriminator Loss: 1.4531... Generator Loss: 0.7460
Epoch 1/1... Discriminator Loss: 1.3910... Generator Loss: 0.7259
Epoch 1/1... Discriminator Loss: 1.3300... Generator Loss: 0.6902
Epoch 1/1... Discriminator Loss: 1.4501... Generator Loss: 0.6132
Epoch 1/1... Discriminator Loss: 1.4678... Generator Loss: 0.7070
Epoch 1/1... Discriminator Loss: 1.3773... Generator Loss: 0.7334
Epoch 1/1... Discriminator Loss: 1.4277... Generator Loss: 0.7005
Epoch 1/1... Discriminator Loss: 1.3636... Generator Loss: 0.7202
Epoch 1/1... Discriminator Loss: 1.4160... Generator Loss: 0.7115
Epoch 1/1... Discriminator Loss: 1.4307... Generator Loss: 0.6325
Epoch 1/1... Discriminator Loss: 1.3495... Generator Loss: 0.7506
Epoch 1/1... Discriminator Loss: 1.4191... Generator Loss: 0.6924
Epoch 1/1... Discriminator Loss: 1.3764... Generator Loss: 0.7104
Epoch 1/1... Discriminator Loss: 1.3986... Generator Loss: 0.6933
Epoch 1/1... Discriminator Loss: 1.3822... Generator Loss: 0.6247
Epoch 1/1... Discriminator Loss: 1.3937... Generator Loss: 0.6902
Epoch 1/1... Discriminator Loss: 1.3934... Generator Loss: 0.6994
Epoch 1/1... Discriminator Loss: 1.4600... Generator Loss: 0.6719
Epoch 1/1... Discriminator Loss: 1.4421... Generator Loss: 0.6858
Epoch 1/1... Discriminator Loss: 1.3752... Generator Loss: 0.7328
Epoch 1/1... Discriminator Loss: 1.3946... Generator Loss: 0.7075
Epoch 1/1... Discriminator Loss: 1.3924... Generator Loss: 0.7037
Epoch 1/1... Discriminator Loss: 1.4491... Generator Loss: 0.6544
Epoch 1/1... Discriminator Loss: 1.3712... Generator Loss: 0.6875
Epoch 1/1... Discriminator Loss: 1.3990... Generator Loss: 0.6518
Epoch 1/1... Discriminator Loss: 1.4055... Generator Loss: 0.7112
Epoch 1/1... Discriminator Loss: 1.4123... Generator Loss: 0.7168
Epoch 1/1... Discriminator Loss: 1.3228... Generator Loss: 0.6819
Epoch 1/1... Discriminator Loss: 1.4285... Generator Loss: 0.7032
Epoch 1/1... Discriminator Loss: 1.4278... Generator Loss: 0.6729
Epoch 1/1... Discriminator Loss: 1.3818... Generator Loss: 0.6842
Epoch 1/1... Discriminator Loss: 1.4253... Generator Loss: 0.7123
Epoch 1/1... Discriminator Loss: 1.3398... Generator Loss: 0.6843
Epoch 1/1... Discriminator Loss: 1.3799... Generator Loss: 0.6894
Epoch 1/1... Discriminator Loss: 1.4929... Generator Loss: 0.6713
Epoch 1/1... Discriminator Loss: 1.3837... Generator Loss: 0.6705
Epoch 1/1... Discriminator Loss: 1.3893... Generator Loss: 0.7127
Epoch 1/1... Discriminator Loss: 1.3622... Generator Loss: 0.6976
Epoch 1/1... Discriminator Loss: 1.4429... Generator Loss: 0.6152
Epoch 1/1... Discriminator Loss: 1.4086... Generator Loss: 0.6521
Epoch 1/1... Discriminator Loss: 1.3989... Generator Loss: 0.6877
Epoch 1/1... Discriminator Loss: 1.4353... Generator Loss: 0.6766
Epoch 1/1... Discriminator Loss: 1.3788... Generator Loss: 0.7352
Epoch 1/1... Discriminator Loss: 1.3822... Generator Loss: 0.7096
Epoch 1/1... Discriminator Loss: 1.4014... Generator Loss: 0.7035
Epoch 1/1... Discriminator Loss: 1.4046... Generator Loss: 0.6751
Epoch 1/1... Discriminator Loss: 1.3929... Generator Loss: 0.6845
Epoch 1/1... Discriminator Loss: 1.4165... Generator Loss: 0.6518
Epoch 1/1... Discriminator Loss: 1.4122... Generator Loss: 0.6997
Epoch 1/1... Discriminator Loss: 1.3945... Generator Loss: 0.7433
Epoch 1/1... Discriminator Loss: 1.3876... Generator Loss: 0.6351
Epoch 1/1... Discriminator Loss: 1.3734... Generator Loss: 0.7079
Epoch 1/1... Discriminator Loss: 1.4124... Generator Loss: 0.7477
Epoch 1/1... Discriminator Loss: 1.4186... Generator Loss: 0.7046
Epoch 1/1... Discriminator Loss: 1.4443... Generator Loss: 0.6499
Epoch 1/1... Discriminator Loss: 1.3726... Generator Loss: 0.7067
Epoch 1/1... Discriminator Loss: 1.4780... Generator Loss: 0.6518
Epoch 1/1... Discriminator Loss: 1.3970... Generator Loss: 0.6611
Epoch 1/1... Discriminator Loss: 1.3285... Generator Loss: 0.7536
Epoch 1/1... Discriminator Loss: 1.4299... Generator Loss: 0.6870
Epoch 1/1... Discriminator Loss: 1.4742... Generator Loss: 0.6231
Epoch 1/1... Discriminator Loss: 1.3178... Generator Loss: 0.7778
Epoch 1/1... Discriminator Loss: 1.5041... Generator Loss: 0.6349
Epoch 1/1... Discriminator Loss: 1.3962... Generator Loss: 0.6836
Epoch 1/1... Discriminator Loss: 1.4370... Generator Loss: 0.6488
Epoch 1/1... Discriminator Loss: 1.4029... Generator Loss: 0.6598
Epoch 1/1... Discriminator Loss: 1.3753... Generator Loss: 0.6957
Epoch 1/1... Discriminator Loss: 1.3738... Generator Loss: 0.7062
Epoch 1/1... Discriminator Loss: 1.4202... Generator Loss: 0.6613
Epoch 1/1... Discriminator Loss: 1.3812... Generator Loss: 0.6657
Epoch 1/1... Discriminator Loss: 1.4295... Generator Loss: 0.7126
Epoch 1/1... Discriminator Loss: 1.3865... Generator Loss: 0.7429
Epoch 1/1... Discriminator Loss: 1.4170... Generator Loss: 0.7240
Epoch 1/1... Discriminator Loss: 1.3623... Generator Loss: 0.6586
Epoch 1/1... Discriminator Loss: 1.3697... Generator Loss: 0.7028
Epoch 1/1... Discriminator Loss: 1.4097... Generator Loss: 0.6804
Epoch 1/1... Discriminator Loss: 1.4085... Generator Loss: 0.7050
Epoch 1/1... Discriminator Loss: 1.4114... Generator Loss: 0.7080
Epoch 1/1... Discriminator Loss: 1.4057... Generator Loss: 0.6704
Epoch 1/1... Discriminator Loss: 1.4318... Generator Loss: 0.6701
Epoch 1/1... Discriminator Loss: 1.4475... Generator Loss: 0.6155
Epoch 1/1... Discriminator Loss: 1.3820... Generator Loss: 0.6580
Epoch 1/1... Discriminator Loss: 1.4359... Generator Loss: 0.6884
Epoch 1/1... Discriminator Loss: 1.3947... Generator Loss: 0.7134
Epoch 1/1... Discriminator Loss: 1.4169... Generator Loss: 0.6731
Epoch 1/1... Discriminator Loss: 1.3723... Generator Loss: 0.7307
Epoch 1/1... Discriminator Loss: 1.4652... Generator Loss: 0.6462
Epoch 1/1... Discriminator Loss: 1.4538... Generator Loss: 0.6225
Epoch 1/1... Discriminator Loss: 1.4444... Generator Loss: 0.6876
Epoch 1/1... Discriminator Loss: 1.4474... Generator Loss: 0.6536
Epoch 1/1... Discriminator Loss: 1.4138... Generator Loss: 0.6648
Epoch 1/1... Discriminator Loss: 1.4392... Generator Loss: 0.6686
Epoch 1/1... Discriminator Loss: 1.3818... Generator Loss: 0.7357
Epoch 1/1... Discriminator Loss: 1.4604... Generator Loss: 0.6188
Epoch 1/1... Discriminator Loss: 1.4181... Generator Loss: 0.5978
Epoch 1/1... Discriminator Loss: 1.4288... Generator Loss: 0.6079
Epoch 1/1... Discriminator Loss: 1.4387... Generator Loss: 0.6821
Epoch 1/1... Discriminator Loss: 1.4237... Generator Loss: 0.7433
Epoch 1/1... Discriminator Loss: 1.3937... Generator Loss: 0.7440
Epoch 1/1... Discriminator Loss: 1.4062... Generator Loss: 0.6866
Epoch 1/1... Discriminator Loss: 1.4038... Generator Loss: 0.6717
Epoch 1/1... Discriminator Loss: 1.4066... Generator Loss: 0.6930
Epoch 1/1... Discriminator Loss: 1.4281... Generator Loss: 0.7045
Epoch 1/1... Discriminator Loss: 1.3766... Generator Loss: 0.6732
Epoch 1/1... Discriminator Loss: 1.4885... Generator Loss: 0.6293
Epoch 1/1... Discriminator Loss: 1.4073... Generator Loss: 0.7000
Epoch 1/1... Discriminator Loss: 1.4218... Generator Loss: 0.6543
Epoch 1/1... Discriminator Loss: 1.4044... Generator Loss: 0.7007
Epoch 1/1... Discriminator Loss: 1.4339... Generator Loss: 0.6844
Epoch 1/1... Discriminator Loss: 1.4186... Generator Loss: 0.6856
Epoch 1/1... Discriminator Loss: 1.4085... Generator Loss: 0.6877
Epoch 1/1... Discriminator Loss: 1.3661... Generator Loss: 0.6917
Epoch 1/1... Discriminator Loss: 1.4331... Generator Loss: 0.6325
Epoch 1/1... Discriminator Loss: 1.4131... Generator Loss: 0.7024
Epoch 1/1... Discriminator Loss: 1.3697... Generator Loss: 0.7095
Epoch 1/1... Discriminator Loss: 1.4313... Generator Loss: 0.7226
Epoch 1/1... Discriminator Loss: 1.3821... Generator Loss: 0.7004
Epoch 1/1... Discriminator Loss: 1.3482... Generator Loss: 0.7515
Epoch 1/1... Discriminator Loss: 1.4203... Generator Loss: 0.6359
Epoch 1/1... Discriminator Loss: 1.5803... Generator Loss: 0.5554
Epoch 1/1... Discriminator Loss: 1.4177... Generator Loss: 0.6960
Epoch 1/1... Discriminator Loss: 1.3841... Generator Loss: 0.7097
Epoch 1/1... Discriminator Loss: 1.4460... Generator Loss: 0.6958
Epoch 1/1... Discriminator Loss: 1.4252... Generator Loss: 0.5960
Epoch 1/1... Discriminator Loss: 1.3989... Generator Loss: 0.7109
Epoch 1/1... Discriminator Loss: 1.4158... Generator Loss: 0.6609
Epoch 1/1... Discriminator Loss: 1.4323... Generator Loss: 0.6535
Epoch 1/1... Discriminator Loss: 1.3957... Generator Loss: 0.7392
Epoch 1/1... Discriminator Loss: 1.4427... Generator Loss: 0.6939
Epoch 1/1... Discriminator Loss: 1.4149... Generator Loss: 0.6746
Epoch 1/1... Discriminator Loss: 1.3746... Generator Loss: 0.7418
Epoch 1/1... Discriminator Loss: 1.4518... Generator Loss: 0.6630
Epoch 1/1... Discriminator Loss: 1.3866... Generator Loss: 0.6678
Epoch 1/1... Discriminator Loss: 1.4202... Generator Loss: 0.6975
Epoch 1/1... Discriminator Loss: 1.3629... Generator Loss: 0.7495
Epoch 1/1... Discriminator Loss: 1.4612... Generator Loss: 0.6315
Epoch 1/1... Discriminator Loss: 1.3917... Generator Loss: 0.6886
Epoch 1/1... Discriminator Loss: 1.3722... Generator Loss: 0.7337
Epoch 1/1... Discriminator Loss: 1.4455... Generator Loss: 0.6253
Epoch 1/1... Discriminator Loss: 1.3819... Generator Loss: 0.6669
Epoch 1/1... Discriminator Loss: 1.4489... Generator Loss: 0.6747
Epoch 1/1... Discriminator Loss: 1.3715... Generator Loss: 0.7226
Epoch 1/1... Discriminator Loss: 1.3955... Generator Loss: 0.6923
Epoch 1/1... Discriminator Loss: 1.4673... Generator Loss: 0.7181
Epoch 1/1... Discriminator Loss: 1.4021... Generator Loss: 0.6646

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.